Breaking Down the REIT Market: Is Social Media Capable of Predicting a REITs’ Performance?

Sophia Bodensteiner, Lukas Lautenschlaeger and Wolfgang Schäfers

ERES from European Real Estate Society (ERES)

Abstract: Twitter is established as a major platform for sharing information and opinions online. This research explores the impact of the sentiment expressed on Twitter on the indirect U.S. real estate market, particularly focusing on financial metrics such as returns and volatility. It analyzes how Twitter sentiment correlates with the overall indirect market and additionally focuses on the corporate level, investigating if general findings are also applicable on an individual company basis. Given by the nature of Twitter messages, comprehensive natural language processing is applied to clean and identify relevant posts and to provide the foundation for extracting the sentiment. The complex linguistic features of the given informal language are handled by using three different approaches for classification including traditional and advanced machine-learning methods. Preliminary results suggest that social media sentiment holds predictive value for both market trends and corporate-level changes. Moreover, they indicate towards changing dynamics in the impact of market sentiment on performance metrics during a crisis, exemplified by the 2020 COVID-19 pandemic. This research additionally highlights the effectiveness of classical dictionary-based approaches for sentiment analysis but also shows the capabilities of more sophisticated classifiers.

Keywords: Machine Learning; REITs; Sentiment Analysis; Twitter (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2024-01-01
New Economics Papers: this item is included in nep-big and nep-cmp
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